Life Cycle Assessment and Social Benefits of Producing Bioplastic Polyhydroxyalkanoates (PHAs) via Integrating Electrochemical CO <sub>2</sub> Conversion and Microbial Fermentation
Bibliographic record
Abstract
Integrating the carbon dioxide reduction reaction (CO 2 RR) with fermentation to produce polyhydroxyalkanoates (PHAs) offers a novel and cutting-edge approach to synthesizing bioplastics compared with other state-of-the-art technologies, such as fossil fuel-based plastics and other renewable-sourced plastic production routes. However, the sustainability of this type of integrated chemical and biological process has not been quantitatively assessed, and the environmental impacts and potential social benefits have not been analyzed. In this study, rigorous life cycle analyses and a comprehensive environmental impact analysis were performed to evaluate CO 2 RR-based PHA products, encompassing both raw material sources and polymer production. Sensitivity and scenario analyses were conducted to evaluate its potential for sustainability, including the social benefits associated with end-of-life management. In the base scenario using the US electricity mix without byproduct displacement, the system resulted in net emissions of 176.3 kg of CO 2 e per kg of PHA, which establishes the baseline for comparison. The results show that, compared to fossil fuel-based plastics, CO 2 RR-based PHA has the potential to reduce carbon emissions by up to 80.34 kg of CO 2 e/kg of PHA when utilizing renewable energy and byproducts. The identified factors, such as PHA yield, the intermediate (C2+) production rate, and nutrient utilization, are key parameters responsible for up to 108% of the variance in greenhouse gas (GHG) emissions and other environmental performance. Considering the conversion and end-of-life management, CO 2 RR-based PHA has the potential to reduce up to 84.84 kg CO 2 e/kg PHA. In terms of social benefits, this process can avoid total social damage costs of about $1.45 trillion, which is nearly twice the global plastic industry market value. However, under less favorable conditions, the process could increase emissions, resulting in an additional $69 billion in social damage. Overall, the findings suggest that converting CO 2 to PHA via an electro-biointegrated pathway offers a promising pathway to reduce GHG emissions and associated environmental and social impacts compared to fossil-based and other renewable plastics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".